Overview

Dataset statistics

Number of variables43
Number of observations12981
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory344.0 B

Variable types

Categorical28
Numeric14
DateTime1

Warnings

lifecycle:transition has constant value "complete" Constant
case has a high cardinality: 776 distinct values High cardinality
hour is highly correlated with timesincemidnightHigh correlation
timesincemidnight is highly correlated with hourHigh correlation
SIRSCritHeartRate is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticSputum is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticBlood is highly correlated with DiagnosticLiquor and 4 other fieldsHigh correlation
InfectionSuspected is highly correlated with SIRSCriteria2OrMore and 4 other fieldsHigh correlation
SIRSCriteria2OrMore is highly correlated with InfectionSuspected and 4 other fieldsHigh correlation
DiagnosticLiquor is highly correlated with SIRSCritHeartRate and 22 other fieldsHigh correlation
concept:name is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
SIRSCritTemperature is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Infusion is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Oligurie is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Hypoxie is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticArtAstrup is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticUrinarySediment is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
Hypotensie is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
lifecycle:transition is highly correlated with SIRSCritHeartRate and 25 other fieldsHigh correlation
Diagnose is highly correlated with lifecycle:transitionHigh correlation
DiagnosticUrinaryCulture is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
SIRSCritLeucos is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
org:group is highly correlated with lifecycle:transitionHigh correlation
DiagnosticIC is highly correlated with DiagnosticBlood and 6 other fieldsHigh correlation
label is highly correlated with lifecycle:transitionHigh correlation
DiagnosticOther is highly correlated with SIRSCritHeartRate and 22 other fieldsHigh correlation
DiagnosticECG is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
DisfuncOrg is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
DiagnosticLacticAcid is highly correlated with DiagnosticBlood and 4 other fieldsHigh correlation
DiagnosticXthorax is highly correlated with DiagnosticLiquor and 3 other fieldsHigh correlation
SIRSCritTachypnea is highly correlated with DiagnosticLiquor and 2 other fieldsHigh correlation
Leucocytes has 3071 (23.7%) zeros Zeros
CRP has 3430 (26.4%) zeros Zeros
LacticAcid has 4048 (31.2%) zeros Zeros
weekday has 1960 (15.1%) zeros Zeros
hour has 186 (1.4%) zeros Zeros
timesincelast has 4748 (36.6%) zeros Zeros
timesincestart has 794 (6.1%) zeros Zeros
remainingtime has 484 (3.7%) zeros Zeros

Reproduction

Analysis started2021-03-23 07:50:16.394941
Analysis finished2021-03-23 07:50:52.497188
Duration36.1 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

InfectionSuspected
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
688
False
 
88

Length

Max length5
Median length5
Mean length4.946999461
Min length4

Characters and Unicode

Total characters64217
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True688
 
5.3%
False88
 
0.7%
2021-03-23T08:50:52.634503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:52.692760image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true688
 
5.3%
false88
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12893
20.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T688
 
1.1%
u688
 
1.1%
F88
 
0.1%
a88
 
0.1%
l88
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63441
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.5%
r12893
20.3%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u688
 
1.1%
a88
 
0.1%
l88
 
0.1%
s88
 
0.1%
ValueCountFrequency (%)
T688
88.7%
F88
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Latin64217
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12893
20.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T688
 
1.1%
u688
 
1.1%
F88
 
0.1%
a88
 
0.1%
l88
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64217
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12893
20.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T688
 
1.1%
u688
 
1.1%
F88
 
0.1%
a88
 
0.1%
l88
 
0.1%

org:group
Categorical

HIGH CORRELATION

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
B
7322 
A
2719 
C
777 
E
776 
F
 
210
Other values (19)
1177 

Length

Max length5
Median length1
Mean length1.000616285
Min length1

Characters and Unicode

Total characters12989
Distinct characters28
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowB
3rd rowB
4th rowB
5th rowC
ValueCountFrequency (%)
B7322
56.4%
A2719
 
20.9%
C777
 
6.0%
E776
 
6.0%
F210
 
1.6%
O184
 
1.4%
G137
 
1.1%
L130
 
1.0%
I124
 
1.0%
M82
 
0.6%
Other values (14)520
 
4.0%
2021-03-23T08:50:52.877279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b7322
56.4%
a2719
 
20.9%
c777
 
6.0%
e776
 
6.0%
f210
 
1.6%
o184
 
1.4%
g137
 
1.1%
l130
 
1.0%
i124
 
1.0%
m82
 
0.6%
Other values (14)520
 
4.0%

Most occurring characters

ValueCountFrequency (%)
B7322
56.4%
A2719
 
20.9%
C777
 
6.0%
E776
 
6.0%
F210
 
1.6%
O184
 
1.4%
G137
 
1.1%
L130
 
1.0%
I124
 
1.0%
M82
 
0.6%
Other values (18)528
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter12979
99.9%
Lowercase Letter10
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
B7322
56.4%
A2719
 
20.9%
C777
 
6.0%
E776
 
6.0%
F210
 
1.6%
O184
 
1.4%
G137
 
1.1%
L130
 
1.0%
I124
 
1.0%
M82
 
0.6%
Other values (13)518
 
4.0%
ValueCountFrequency (%)
o2
20.0%
t2
20.0%
h2
20.0%
e2
20.0%
r2
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12989
100.0%

Most frequent character per script

ValueCountFrequency (%)
B7322
56.4%
A2719
 
20.9%
C777
 
6.0%
E776
 
6.0%
F210
 
1.6%
O184
 
1.4%
G137
 
1.1%
L130
 
1.0%
I124
 
1.0%
M82
 
0.6%
Other values (18)528
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII12989
100.0%

Most frequent character per block

ValueCountFrequency (%)
B7322
56.4%
A2719
 
20.9%
C777
 
6.0%
E776
 
6.0%
F210
 
1.6%
O184
 
1.4%
G137
 
1.1%
L130
 
1.0%
I124
 
1.0%
M82
 
0.6%
Other values (18)528
 
4.1%

DiagnosticBlood
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
669
False
 
107

Length

Max length5
Median length5
Mean length4.948463138
Min length4

Characters and Unicode

Total characters64236
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True669
 
5.2%
False107
 
0.8%
2021-03-23T08:50:53.060457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:53.117849image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true669
 
5.2%
false107
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12874
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T669
 
1.0%
u669
 
1.0%
F107
 
0.2%
a107
 
0.2%
l107
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63460
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.5%
r12874
20.3%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u669
 
1.1%
a107
 
0.2%
l107
 
0.2%
s107
 
0.2%
ValueCountFrequency (%)
T669
86.2%
F107
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin64236
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12874
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T669
 
1.0%
u669
 
1.0%
F107
 
0.2%
a107
 
0.2%
l107
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64236
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12874
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T669
 
1.0%
u669
 
1.0%
F107
 
0.2%
a107
 
0.2%
l107
 
0.2%

DisfuncOrg
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
False
 
720
True
 
56

Length

Max length5
Median length5
Mean length4.995686003
Min length4

Characters and Unicode

Total characters64849
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
False720
 
5.5%
True56
 
0.4%
2021-03-23T08:50:53.276068image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:53.333076image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
false720
 
5.5%
true56
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e12981
20.0%
r12261
18.9%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F720
 
1.1%
a720
 
1.1%
l720
 
1.1%
s720
 
1.1%
T56
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64073
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.3%
r12261
19.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
a720
 
1.1%
l720
 
1.1%
s720
 
1.1%
u56
 
0.1%
ValueCountFrequency (%)
F720
92.8%
T56
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin64849
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.0%
r12261
18.9%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F720
 
1.1%
a720
 
1.1%
l720
 
1.1%
s720
 
1.1%
T56
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64849
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.0%
r12261
18.9%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F720
 
1.1%
a720
 
1.1%
l720
 
1.1%
s720
 
1.1%
T56
 
0.1%

SIRSCritTachypnea
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
494
False
 
282

Length

Max length5
Median length5
Mean length4.96194438
Min length4

Characters and Unicode

Total characters64411
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True494
 
3.8%
False282
 
2.2%
2021-03-23T08:50:53.490252image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:53.547809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true494
 
3.8%
false282
 
2.2%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12699
19.7%
o12205
18.9%
t12205
18.9%
h12205
18.9%
T494
 
0.8%
u494
 
0.8%
F282
 
0.4%
a282
 
0.4%
l282
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63635
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.4%
r12699
20.0%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u494
 
0.8%
a282
 
0.4%
l282
 
0.4%
s282
 
0.4%
ValueCountFrequency (%)
T494
63.7%
F282
36.3%

Most occurring scripts

ValueCountFrequency (%)
Latin64411
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12699
19.7%
o12205
18.9%
t12205
18.9%
h12205
18.9%
T494
 
0.8%
u494
 
0.8%
F282
 
0.4%
a282
 
0.4%
l282
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII64411
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12699
19.7%
o12205
18.9%
t12205
18.9%
h12205
18.9%
T494
 
0.8%
u494
 
0.8%
F282
 
0.4%
a282
 
0.4%
l282
 
0.4%

Hypotensie
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
False
 
725
True
 
51

Length

Max length5
Median length5
Mean length4.996071181
Min length4

Characters and Unicode

Total characters64854
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
False725
 
5.6%
True51
 
0.4%
2021-03-23T08:50:53.713998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:53.774838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
false725
 
5.6%
true51
 
0.4%

Most occurring characters

ValueCountFrequency (%)
e12981
20.0%
r12256
18.9%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F725
 
1.1%
a725
 
1.1%
l725
 
1.1%
s725
 
1.1%
T51
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64078
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.3%
r12256
19.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
a725
 
1.1%
l725
 
1.1%
s725
 
1.1%
u51
 
0.1%
ValueCountFrequency (%)
F725
93.4%
T51
 
6.6%

Most occurring scripts

ValueCountFrequency (%)
Latin64854
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.0%
r12256
18.9%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F725
 
1.1%
a725
 
1.1%
l725
 
1.1%
s725
 
1.1%
T51
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64854
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.0%
r12256
18.9%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F725
 
1.1%
a725
 
1.1%
l725
 
1.1%
s725
 
1.1%
T51
 
0.1%

SIRSCritHeartRate
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
645
False
 
131

Length

Max length5
Median length5
Mean length4.950311994
Min length4

Characters and Unicode

Total characters64260
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True645
 
5.0%
False131
 
1.0%
2021-03-23T08:50:53.939409image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:53.998677image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true645
 
5.0%
false131
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12850
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T645
 
1.0%
u645
 
1.0%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63484
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.4%
r12850
20.2%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u645
 
1.0%
a131
 
0.2%
l131
 
0.2%
s131
 
0.2%
ValueCountFrequency (%)
T645
83.1%
F131
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
Latin64260
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12850
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T645
 
1.0%
u645
 
1.0%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64260
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12850
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T645
 
1.0%
u645
 
1.0%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Infusion
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
656
False
 
120

Length

Max length5
Median length5
Mean length4.949464602
Min length4

Characters and Unicode

Total characters64249
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True656
 
5.1%
False120
 
0.9%
2021-03-23T08:50:54.157488image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:54.215863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true656
 
5.1%
false120
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12861
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T656
 
1.0%
u656
 
1.0%
F120
 
0.2%
a120
 
0.2%
l120
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63473
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.5%
r12861
20.3%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u656
 
1.0%
a120
 
0.2%
l120
 
0.2%
s120
 
0.2%
ValueCountFrequency (%)
T656
84.5%
F120
 
15.5%

Most occurring scripts

ValueCountFrequency (%)
Latin64249
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12861
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T656
 
1.0%
u656
 
1.0%
F120
 
0.2%
a120
 
0.2%
l120
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64249
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12861
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T656
 
1.0%
u656
 
1.0%
F120
 
0.2%
a120
 
0.2%
l120
 
0.2%

DiagnosticArtAstrup
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
False
 
534
True
 
242

Length

Max length5
Median length5
Mean length4.981357368
Min length4

Characters and Unicode

Total characters64663
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
False534
 
4.1%
True242
 
1.9%
2021-03-23T08:50:54.375565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:54.434117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
false534
 
4.1%
true242
 
1.9%

Most occurring characters

ValueCountFrequency (%)
e12981
20.1%
r12447
19.2%
o12205
18.9%
t12205
18.9%
h12205
18.9%
F534
 
0.8%
a534
 
0.8%
l534
 
0.8%
s534
 
0.8%
T242
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63887
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.3%
r12447
19.5%
o12205
19.1%
t12205
19.1%
h12205
19.1%
a534
 
0.8%
l534
 
0.8%
s534
 
0.8%
u242
 
0.4%
ValueCountFrequency (%)
F534
68.8%
T242
31.2%

Most occurring scripts

ValueCountFrequency (%)
Latin64663
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.1%
r12447
19.2%
o12205
18.9%
t12205
18.9%
h12205
18.9%
F534
 
0.8%
a534
 
0.8%
l534
 
0.8%
s534
 
0.8%
T242
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII64663
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.1%
r12447
19.2%
o12205
18.9%
t12205
18.9%
h12205
18.9%
F534
 
0.8%
a534
 
0.8%
l534
 
0.8%
s534
 
0.8%
T242
 
0.4%

concept:name
Categorical

HIGH CORRELATION

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Leucocytes
3072 
CRP
2974 
LacticAcid
1276 
Admission NC
1145 
ER Triage
777 
Other values (9)
3737 

Length

Max length16
Median length10
Mean length9.288575611
Min length3

Characters and Unicode

Total characters120575
Distinct characters32
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowER Registration
2nd rowLeucocytes
3rd rowCRP
4th rowLacticAcid
5th rowER Triage
ValueCountFrequency (%)
Leucocytes3072
23.7%
CRP2974
22.9%
LacticAcid1276
9.8%
Admission NC1145
 
8.8%
ER Triage777
 
6.0%
ER Registration776
 
6.0%
ER Sepsis Triage775
 
6.0%
IV Antibiotics678
 
5.2%
Release A671
 
5.2%
IV Liquid620
 
4.8%
Other values (4)217
 
1.7%
2021-03-23T08:50:54.724562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
leucocytes3072
15.8%
crp2974
15.3%
er2328
12.0%
triage1552
8.0%
iv1298
6.7%
lacticacid1276
6.6%
admission1257
6.5%
nc1145
 
5.9%
registration776
 
4.0%
release776
 
4.0%
Other values (8)2961
15.3%

Most occurring characters

ValueCountFrequency (%)
i12219
 
10.1%
e11575
 
9.6%
c10650
 
8.8%
s9366
 
7.8%
t7256
 
6.0%
R6854
 
5.7%
6434
 
5.3%
o5783
 
4.8%
L4968
 
4.1%
a4380
 
3.6%
Other values (22)41090
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter82619
68.5%
Uppercase Letter31522
 
26.1%
Space Separator6434
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
i12219
14.8%
e11575
14.0%
c10650
12.9%
s9366
11.3%
t7256
8.8%
o5783
7.0%
a4380
 
5.3%
u3692
 
4.5%
d3153
 
3.8%
y3072
 
3.7%
Other values (8)11473
13.9%
ValueCountFrequency (%)
R6854
21.7%
L4968
15.8%
C4256
13.5%
A3882
12.3%
P2974
9.4%
E2328
 
7.4%
T1552
 
4.9%
I1410
 
4.5%
V1298
 
4.1%
N1145
 
3.6%
Other values (3)855
 
2.7%
ValueCountFrequency (%)
6434
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin114141
94.7%
Common6434
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
i12219
 
10.7%
e11575
 
10.1%
c10650
 
9.3%
s9366
 
8.2%
t7256
 
6.4%
R6854
 
6.0%
o5783
 
5.1%
L4968
 
4.4%
a4380
 
3.8%
C4256
 
3.7%
Other values (21)36834
32.3%
ValueCountFrequency (%)
6434
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII120575
100.0%

Most frequent character per block

ValueCountFrequency (%)
i12219
 
10.1%
e11575
 
9.6%
c10650
 
8.8%
s9366
 
7.8%
t7256
 
6.0%
R6854
 
5.7%
6434
 
5.3%
o5783
 
4.8%
L4968
 
4.1%
a4380
 
3.6%
Other values (22)41090
34.1%

Age
Real number (ℝ≥0)

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.87350743
Minimum20
Maximum90
Zeros0
Zeros (%)0.0%
Memory size101.5 KiB
2021-03-23T08:50:54.807006image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile40
Q165
median75
Q385
95-th percentile90
Maximum90
Range70
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.13965878
Coefficient of variation (CV)0.2106431051
Kurtosis0.3944739723
Mean71.87350743
Median Absolute Deviation (MAD)10
Skewness-0.9232845989
Sum932990
Variance229.209268
MonotocityNot monotonic
2021-03-23T08:50:54.886689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
851892
14.6%
801878
14.5%
901867
14.4%
751676
12.9%
701500
11.6%
65963
7.4%
60924
7.1%
55784
6.0%
50519
 
4.0%
40294
 
2.3%
Other values (5)684
 
5.3%
ValueCountFrequency (%)
2038
 
0.3%
2587
 
0.7%
30101
 
0.8%
35224
1.7%
40294
2.3%
ValueCountFrequency (%)
901867
14.4%
851892
14.6%
801878
14.5%
751676
12.9%
701500
11.6%

DiagnosticIC
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
686
False
 
90

Length

Max length5
Median length5
Mean length4.947153532
Min length4

Characters and Unicode

Total characters64219
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True686
 
5.3%
False90
 
0.7%
2021-03-23T08:50:55.072458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:55.133489image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true686
 
5.3%
false90
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12891
20.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T686
 
1.1%
u686
 
1.1%
F90
 
0.1%
a90
 
0.1%
l90
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63443
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.5%
r12891
20.3%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u686
 
1.1%
a90
 
0.1%
l90
 
0.1%
s90
 
0.1%
ValueCountFrequency (%)
T686
88.4%
F90
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
Latin64219
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12891
20.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T686
 
1.1%
u686
 
1.1%
F90
 
0.1%
a90
 
0.1%
l90
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64219
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12891
20.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T686
 
1.1%
u686
 
1.1%
F90
 
0.1%
a90
 
0.1%
l90
 
0.1%

DiagnosticSputum
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
False
 
753
True
 
23

Length

Max length5
Median length5
Mean length4.99822818
Min length4

Characters and Unicode

Total characters64882
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
False753
 
5.8%
True23
 
0.2%
2021-03-23T08:50:55.300564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:55.361091image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
false753
 
5.8%
true23
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e12981
20.0%
r12228
18.8%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64106
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.2%
r12228
19.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
u23
 
< 0.1%
ValueCountFrequency (%)
F753
97.0%
T23
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin64882
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.0%
r12228
18.8%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64882
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.0%
r12228
18.8%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

DiagnosticLiquor
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12210 
False
 
771

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters64905
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12210
94.1%
False771
 
5.9%
2021-03-23T08:50:55.512198image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:55.568270image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12210
94.1%
false771
 
5.9%

Most occurring characters

ValueCountFrequency (%)
e12981
20.0%
o12210
18.8%
t12210
18.8%
h12210
18.8%
r12210
18.8%
F771
 
1.2%
a771
 
1.2%
l771
 
1.2%
s771
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64134
98.8%
Uppercase Letter771
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.2%
o12210
19.0%
t12210
19.0%
h12210
19.0%
r12210
19.0%
a771
 
1.2%
l771
 
1.2%
s771
 
1.2%
ValueCountFrequency (%)
F771
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin64905
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.0%
o12210
18.8%
t12210
18.8%
h12210
18.8%
r12210
18.8%
F771
 
1.2%
a771
 
1.2%
l771
 
1.2%
s771
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64905
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.0%
o12210
18.8%
t12210
18.8%
h12210
18.8%
r12210
18.8%
F771
 
1.2%
a771
 
1.2%
l771
 
1.2%
s771
 
1.2%

DiagnosticOther
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12209 
False
 
772

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters64905
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12209
94.1%
False772
 
5.9%
2021-03-23T08:50:55.712193image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:55.768309image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12209
94.1%
false772
 
5.9%

Most occurring characters

ValueCountFrequency (%)
e12981
20.0%
o12209
18.8%
t12209
18.8%
h12209
18.8%
r12209
18.8%
F772
 
1.2%
a772
 
1.2%
l772
 
1.2%
s772
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64133
98.8%
Uppercase Letter772
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.2%
o12209
19.0%
t12209
19.0%
h12209
19.0%
r12209
19.0%
a772
 
1.2%
l772
 
1.2%
s772
 
1.2%
ValueCountFrequency (%)
F772
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin64905
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.0%
o12209
18.8%
t12209
18.8%
h12209
18.8%
r12209
18.8%
F772
 
1.2%
a772
 
1.2%
l772
 
1.2%
s772
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64905
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.0%
o12209
18.8%
t12209
18.8%
h12209
18.8%
r12209
18.8%
F772
 
1.2%
a772
 
1.2%
l772
 
1.2%
s772
 
1.2%

SIRSCriteria2OrMore
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
690
False
 
86

Length

Max length5
Median length5
Mean length4.946845389
Min length4

Characters and Unicode

Total characters64215
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True690
 
5.3%
False86
 
0.7%
2021-03-23T08:50:55.926622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:55.986600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true690
 
5.3%
false86
 
0.7%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12895
20.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T690
 
1.1%
u690
 
1.1%
F86
 
0.1%
a86
 
0.1%
l86
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63439
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.5%
r12895
20.3%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u690
 
1.1%
a86
 
0.1%
l86
 
0.1%
s86
 
0.1%
ValueCountFrequency (%)
T690
88.9%
F86
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Latin64215
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12895
20.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T690
 
1.1%
u690
 
1.1%
F86
 
0.1%
a86
 
0.1%
l86
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64215
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12895
20.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T690
 
1.1%
u690
 
1.1%
F86
 
0.1%
a86
 
0.1%
l86
 
0.1%

DiagnosticXthorax
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
637
False
 
139

Length

Max length5
Median length5
Mean length4.95092828
Min length4

Characters and Unicode

Total characters64268
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True637
 
4.9%
False139
 
1.1%
2021-03-23T08:50:56.148576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:56.206574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true637
 
4.9%
false139
 
1.1%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12842
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T637
 
1.0%
u637
 
1.0%
F139
 
0.2%
a139
 
0.2%
l139
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63492
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.4%
r12842
20.2%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u637
 
1.0%
a139
 
0.2%
l139
 
0.2%
s139
 
0.2%
ValueCountFrequency (%)
T637
82.1%
F139
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Latin64268
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12842
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T637
 
1.0%
u637
 
1.0%
F139
 
0.2%
a139
 
0.2%
l139
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64268
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12842
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T637
 
1.0%
u637
 
1.0%
F139
 
0.2%
a139
 
0.2%
l139
 
0.2%

SIRSCritTemperature
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
645
False
 
131

Length

Max length5
Median length5
Mean length4.950311994
Min length4

Characters and Unicode

Total characters64260
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True645
 
5.0%
False131
 
1.0%
2021-03-23T08:50:56.364772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:56.422686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true645
 
5.0%
false131
 
1.0%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12850
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T645
 
1.0%
u645
 
1.0%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63484
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.4%
r12850
20.2%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u645
 
1.0%
a131
 
0.2%
l131
 
0.2%
s131
 
0.2%
ValueCountFrequency (%)
T645
83.1%
F131
 
16.9%

Most occurring scripts

ValueCountFrequency (%)
Latin64260
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12850
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T645
 
1.0%
u645
 
1.0%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64260
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12850
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T645
 
1.0%
u645
 
1.0%
F131
 
0.2%
a131
 
0.2%
l131
 
0.2%
Distinct7758
Distinct (%)59.8%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
Minimum2013-11-07 07:18:29+00:00
Maximum2015-03-07 10:00:00+00:00
2021-03-23T08:50:56.496984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:56.596979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DiagnosticUrinaryCulture
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
388
False
 
388

Length

Max length5
Median length5
Mean length4.970110161
Min length4

Characters and Unicode

Total characters64517
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True388
 
3.0%
False388
 
3.0%
2021-03-23T08:50:56.912780image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:56.970835image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true388
 
3.0%
false388
 
3.0%

Most occurring characters

ValueCountFrequency (%)
e12981
20.1%
r12593
19.5%
o12205
18.9%
t12205
18.9%
h12205
18.9%
T388
 
0.6%
u388
 
0.6%
F388
 
0.6%
a388
 
0.6%
l388
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63741
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.4%
r12593
19.8%
o12205
19.1%
t12205
19.1%
h12205
19.1%
u388
 
0.6%
a388
 
0.6%
l388
 
0.6%
s388
 
0.6%
ValueCountFrequency (%)
T388
50.0%
F388
50.0%

Most occurring scripts

ValueCountFrequency (%)
Latin64517
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.1%
r12593
19.5%
o12205
18.9%
t12205
18.9%
h12205
18.9%
T388
 
0.6%
u388
 
0.6%
F388
 
0.6%
a388
 
0.6%
l388
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII64517
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.1%
r12593
19.5%
o12205
18.9%
t12205
18.9%
h12205
18.9%
T388
 
0.6%
u388
 
0.6%
F388
 
0.6%
a388
 
0.6%
l388
 
0.6%

SIRSCritLeucos
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
False
 
735
True
 
41

Length

Max length5
Median length5
Mean length4.996841538
Min length4

Characters and Unicode

Total characters64864
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
False735
 
5.7%
True41
 
0.3%
2021-03-23T08:50:57.129785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:57.188047image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
false735
 
5.7%
true41
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e12981
20.0%
r12246
18.9%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F735
 
1.1%
a735
 
1.1%
l735
 
1.1%
s735
 
1.1%
T41
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64088
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.3%
r12246
19.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
a735
 
1.1%
l735
 
1.1%
s735
 
1.1%
u41
 
0.1%
ValueCountFrequency (%)
F735
94.7%
T41
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Latin64864
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.0%
r12246
18.9%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F735
 
1.1%
a735
 
1.1%
l735
 
1.1%
s735
 
1.1%
T41
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64864
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.0%
r12246
18.9%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F735
 
1.1%
a735
 
1.1%
l735
 
1.1%
s735
 
1.1%
T41
 
0.1%

Oligurie
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
False
 
753
True
 
23

Length

Max length5
Median length5
Mean length4.99822818
Min length4

Characters and Unicode

Total characters64882
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
False753
 
5.8%
True23
 
0.2%
2021-03-23T08:50:57.346506image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:57.404392image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
false753
 
5.8%
true23
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e12981
20.0%
r12228
18.8%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64106
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.2%
r12228
19.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
u23
 
< 0.1%
ValueCountFrequency (%)
F753
97.0%
T23
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Latin64882
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.0%
r12228
18.8%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64882
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.0%
r12228
18.8%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F753
 
1.2%
a753
 
1.2%
l753
 
1.2%
s753
 
1.2%
T23
 
< 0.1%

DiagnosticLacticAcid
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
654
False
 
122

Length

Max length5
Median length5
Mean length4.949618673
Min length4

Characters and Unicode

Total characters64251
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True654
 
5.0%
False122
 
0.9%
2021-03-23T08:50:57.562744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:57.621315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true654
 
5.0%
false122
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12859
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T654
 
1.0%
u654
 
1.0%
F122
 
0.2%
a122
 
0.2%
l122
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63475
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.5%
r12859
20.3%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u654
 
1.0%
a122
 
0.2%
l122
 
0.2%
s122
 
0.2%
ValueCountFrequency (%)
T654
84.3%
F122
 
15.7%

Most occurring scripts

ValueCountFrequency (%)
Latin64251
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12859
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T654
 
1.0%
u654
 
1.0%
F122
 
0.2%
a122
 
0.2%
l122
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64251
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12859
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T654
 
1.0%
u654
 
1.0%
F122
 
0.2%
a122
 
0.2%
l122
 
0.2%

lifecycle:transition
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
complete
12981 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters103848
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcomplete
2nd rowcomplete
3rd rowcomplete
4th rowcomplete
5th rowcomplete
ValueCountFrequency (%)
complete12981
100.0%
2021-03-23T08:50:57.765203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:57.818245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
complete12981
100.0%

Most occurring characters

ValueCountFrequency (%)
e25962
25.0%
c12981
12.5%
o12981
12.5%
m12981
12.5%
p12981
12.5%
l12981
12.5%
t12981
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter103848
100.0%

Most frequent character per category

ValueCountFrequency (%)
e25962
25.0%
c12981
12.5%
o12981
12.5%
m12981
12.5%
p12981
12.5%
l12981
12.5%
t12981
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin103848
100.0%

Most frequent character per script

ValueCountFrequency (%)
e25962
25.0%
c12981
12.5%
o12981
12.5%
m12981
12.5%
p12981
12.5%
l12981
12.5%
t12981
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII103848
100.0%

Most frequent character per block

ValueCountFrequency (%)
e25962
25.0%
c12981
12.5%
o12981
12.5%
m12981
12.5%
p12981
12.5%
l12981
12.5%
t12981
12.5%

Diagnose
Categorical

HIGH CORRELATION

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12501 
C
 
143
B
 
81
E
 
64
H
 
49
Other values (7)
 
143

Length

Max length5
Median length5
Mean length4.852091518
Min length1

Characters and Unicode

Total characters62985
Distinct characters16
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowother
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12501
96.3%
C143
 
1.1%
B81
 
0.6%
E64
 
0.5%
H49
 
0.4%
G42
 
0.3%
D23
 
0.2%
K22
 
0.2%
R21
 
0.2%
Q13
 
0.1%
Other values (2)22
 
0.2%
2021-03-23T08:50:57.968999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
other12501
96.3%
c143
 
1.1%
b81
 
0.6%
e64
 
0.5%
h49
 
0.4%
g42
 
0.3%
d23
 
0.2%
k22
 
0.2%
r21
 
0.2%
q13
 
0.1%
Other values (2)22
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o12501
19.8%
t12501
19.8%
h12501
19.8%
e12501
19.8%
r12501
19.8%
C143
 
0.2%
B81
 
0.1%
E64
 
0.1%
H49
 
0.1%
G42
 
0.1%
Other values (6)101
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter62505
99.2%
Uppercase Letter480
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
C143
29.8%
B81
16.9%
E64
13.3%
H49
 
10.2%
G42
 
8.8%
D23
 
4.8%
K22
 
4.6%
R21
 
4.4%
Q13
 
2.7%
S12
 
2.5%
ValueCountFrequency (%)
o12501
20.0%
t12501
20.0%
h12501
20.0%
e12501
20.0%
r12501
20.0%

Most occurring scripts

ValueCountFrequency (%)
Latin62985
100.0%

Most frequent character per script

ValueCountFrequency (%)
o12501
19.8%
t12501
19.8%
h12501
19.8%
e12501
19.8%
r12501
19.8%
C143
 
0.2%
B81
 
0.1%
E64
 
0.1%
H49
 
0.1%
G42
 
0.1%
Other values (6)101
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII62985
100.0%

Most frequent character per block

ValueCountFrequency (%)
o12501
19.8%
t12501
19.8%
h12501
19.8%
e12501
19.8%
r12501
19.8%
C143
 
0.2%
B81
 
0.1%
E64
 
0.1%
H49
 
0.1%
G42
 
0.1%
Other values (6)101
 
0.2%

Hypoxie
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
False
 
760
True
 
16

Length

Max length5
Median length5
Mean length4.998767429
Min length4

Characters and Unicode

Total characters64889
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFalse
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
False760
 
5.9%
True16
 
0.1%
2021-03-23T08:50:58.154843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:58.213168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
false760
 
5.9%
true16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
e12981
20.0%
r12221
18.8%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F760
 
1.2%
a760
 
1.2%
l760
 
1.2%
s760
 
1.2%
T16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter64113
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.2%
r12221
19.1%
o12205
19.0%
t12205
19.0%
h12205
19.0%
a760
 
1.2%
l760
 
1.2%
s760
 
1.2%
u16
 
< 0.1%
ValueCountFrequency (%)
F760
97.9%
T16
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin64889
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.0%
r12221
18.8%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F760
 
1.2%
a760
 
1.2%
l760
 
1.2%
s760
 
1.2%
T16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII64889
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.0%
r12221
18.8%
o12205
18.8%
t12205
18.8%
h12205
18.8%
F760
 
1.2%
a760
 
1.2%
l760
 
1.2%
s760
 
1.2%
T16
 
< 0.1%

DiagnosticUrinarySediment
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
423
False
 
353

Length

Max length5
Median length5
Mean length4.967413913
Min length4

Characters and Unicode

Total characters64482
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True423
 
3.3%
False353
 
2.7%
2021-03-23T08:50:58.371261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:58.429484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true423
 
3.3%
false353
 
2.7%

Most occurring characters

ValueCountFrequency (%)
e12981
20.1%
r12628
19.6%
o12205
18.9%
t12205
18.9%
h12205
18.9%
T423
 
0.7%
u423
 
0.7%
F353
 
0.5%
a353
 
0.5%
l353
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63706
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.4%
r12628
19.8%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u423
 
0.7%
a353
 
0.6%
l353
 
0.6%
s353
 
0.6%
ValueCountFrequency (%)
T423
54.5%
F353
45.5%

Most occurring scripts

ValueCountFrequency (%)
Latin64482
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.1%
r12628
19.6%
o12205
18.9%
t12205
18.9%
h12205
18.9%
T423
 
0.7%
u423
 
0.7%
F353
 
0.5%
a353
 
0.5%
l353
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII64482
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.1%
r12628
19.6%
o12205
18.9%
t12205
18.9%
h12205
18.9%
T423
 
0.7%
u423
 
0.7%
F353
 
0.5%
a353
 
0.5%
l353
 
0.5%

DiagnosticECG
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
other
12205 
True
 
623
False
 
153

Length

Max length5
Median length5
Mean length4.952006779
Min length4

Characters and Unicode

Total characters64282
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTrue
2nd rowother
3rd rowother
4th rowother
5th rowother
ValueCountFrequency (%)
other12205
94.0%
True623
 
4.8%
False153
 
1.2%
2021-03-23T08:50:58.587801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:50:58.645645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
other12205
94.0%
true623
 
4.8%
false153
 
1.2%

Most occurring characters

ValueCountFrequency (%)
e12981
20.2%
r12828
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T623
 
1.0%
u623
 
1.0%
F153
 
0.2%
a153
 
0.2%
l153
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter63506
98.8%
Uppercase Letter776
 
1.2%

Most frequent character per category

ValueCountFrequency (%)
e12981
20.4%
r12828
20.2%
o12205
19.2%
t12205
19.2%
h12205
19.2%
u623
 
1.0%
a153
 
0.2%
l153
 
0.2%
s153
 
0.2%
ValueCountFrequency (%)
T623
80.3%
F153
 
19.7%

Most occurring scripts

ValueCountFrequency (%)
Latin64282
100.0%

Most frequent character per script

ValueCountFrequency (%)
e12981
20.2%
r12828
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T623
 
1.0%
u623
 
1.0%
F153
 
0.2%
a153
 
0.2%
l153
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII64282
100.0%

Most frequent character per block

ValueCountFrequency (%)
e12981
20.2%
r12828
20.0%
o12205
19.0%
t12205
19.0%
h12205
19.0%
T623
 
1.0%
u623
 
1.0%
F153
 
0.2%
a153
 
0.2%
l153
 
0.2%

case
Categorical

HIGH CARDINALITY

Distinct776
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
NGA
 
185
KM
 
169
OD
 
118
GK
 
88
YX
 
84
Other values (771)
12337 

Length

Max length7
Median length2
Mean length2.311301132
Min length1

Characters and Unicode

Total characters30003
Distinct characters31
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA
ValueCountFrequency (%)
NGA185
 
1.4%
KM169
 
1.3%
OD118
 
0.9%
GK88
 
0.7%
YX84
 
0.6%
ZMA66
 
0.5%
YLA60
 
0.5%
NZ60
 
0.5%
GF58
 
0.4%
MKA52
 
0.4%
Other values (766)12041
92.8%
2021-03-23T08:50:58.835990image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nga185
 
1.4%
km169
 
1.3%
od118
 
0.9%
gk88
 
0.7%
yx84
 
0.6%
zma66
 
0.5%
nz60
 
0.5%
yla60
 
0.5%
gf58
 
0.4%
mka52
 
0.4%
Other values (766)12041
92.8%

Most occurring characters

ValueCountFrequency (%)
A5060
 
16.9%
K1434
 
4.8%
G1386
 
4.6%
M1319
 
4.4%
H1138
 
3.8%
D1136
 
3.8%
N1129
 
3.8%
L1120
 
3.7%
B1118
 
3.7%
I1095
 
3.6%
Other values (21)14068
46.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter29835
99.4%
Lowercase Letter168
 
0.6%

Most frequent character per category

ValueCountFrequency (%)
A5060
 
17.0%
K1434
 
4.8%
G1386
 
4.6%
M1319
 
4.4%
H1138
 
3.8%
D1136
 
3.8%
N1129
 
3.8%
L1120
 
3.8%
B1118
 
3.7%
I1095
 
3.7%
Other values (16)13900
46.6%
ValueCountFrequency (%)
i48
28.6%
s48
28.6%
m24
14.3%
n24
14.3%
g24
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin30003
100.0%

Most frequent character per script

ValueCountFrequency (%)
A5060
 
16.9%
K1434
 
4.8%
G1386
 
4.6%
M1319
 
4.4%
H1138
 
3.8%
D1136
 
3.8%
N1129
 
3.8%
L1120
 
3.7%
B1118
 
3.7%
I1095
 
3.6%
Other values (21)14068
46.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII30003
100.0%

Most frequent character per block

ValueCountFrequency (%)
A5060
 
16.9%
K1434
 
4.8%
G1386
 
4.6%
M1319
 
4.4%
H1138
 
3.8%
D1136
 
3.8%
N1129
 
3.8%
L1120
 
3.7%
B1118
 
3.7%
I1095
 
3.6%
Other values (21)14068
46.9%

Leucocytes
Real number (ℝ≥0)

ZEROS

Distinct354
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.14820122
Minimum0
Maximum381.3
Zeros3071
Zeros (%)23.7%
Memory size101.5 KiB
2021-03-23T08:50:58.932366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.3
median9.3
Q313.9
95-th percentile23.9
Maximum381.3
Range381.3
Interquartile range (IQR)12.6

Descriptive statistics

Standard deviation13.76919182
Coefficient of variation (CV)1.356811077
Kurtosis208.8323007
Mean10.14820122
Median Absolute Deviation (MAD)5.4
Skewness11.04373537
Sum131733.8
Variance189.5906433
MonotocityNot monotonic
2021-03-23T08:50:59.164520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03071
 
23.7%
11.4114
 
0.9%
10.1102
 
0.8%
9.8101
 
0.8%
8.7100
 
0.8%
1095
 
0.7%
8.593
 
0.7%
10.990
 
0.7%
6.790
 
0.7%
8.289
 
0.7%
Other values (344)9036
69.6%
ValueCountFrequency (%)
03071
23.7%
0.214
 
0.1%
0.316
 
0.1%
0.45
 
< 0.1%
0.521
 
0.2%
ValueCountFrequency (%)
381.32
 
< 0.1%
297.61
 
< 0.1%
296.25
< 0.1%
234.22
 
< 0.1%
199.82
 
< 0.1%

CRP
Real number (ℝ≥0)

ZEROS

Distinct365
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.44896387
Minimum0
Maximum573
Zeros3430
Zeros (%)26.4%
Memory size101.5 KiB
2021-03-23T08:50:59.282650image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median58
Q3137
95-th percentile280
Maximum573
Range573
Interquartile range (IQR)137

Descriptive statistics

Standard deviation94.1459827
Coefficient of variation (CV)1.101780273
Kurtosis1.338559483
Mean85.44896387
Median Absolute Deviation (MAD)58
Skewness1.268287351
Sum1109213
Variance8863.466058
MonotocityNot monotonic
2021-03-23T08:50:59.389961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03430
 
26.4%
8128
 
1.0%
17111
 
0.9%
1199
 
0.8%
992
 
0.7%
2087
 
0.7%
686
 
0.7%
1283
 
0.6%
1482
 
0.6%
1678
 
0.6%
Other values (355)8705
67.1%
ValueCountFrequency (%)
03430
26.4%
540
 
0.3%
686
 
0.7%
775
 
0.6%
8128
 
1.0%
ValueCountFrequency (%)
5734
< 0.1%
5165
< 0.1%
5074
< 0.1%
4783
 
< 0.1%
4778
0.1%

LacticAcid
Real number (ℝ≥0)

ZEROS

Distinct75
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.240335876
Minimum0
Maximum14.9
Zeros4048
Zeros (%)31.2%
Memory size101.5 KiB
2021-03-23T08:50:59.503895image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.2
Q31.8
95-th percentile3.2
Maximum14.9
Range14.9
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.291492852
Coefficient of variation (CV)1.041244454
Kurtosis15.26148115
Mean1.240335876
Median Absolute Deviation (MAD)0.9
Skewness2.543226266
Sum16100.8
Variance1.667953785
MonotocityNot monotonic
2021-03-23T08:50:59.613003image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04048
31.2%
1.1610
 
4.7%
1.2602
 
4.6%
1.3552
 
4.3%
1.5528
 
4.1%
1466
 
3.6%
1.4459
 
3.5%
1.9426
 
3.3%
1.6420
 
3.2%
0.9389
 
3.0%
Other values (65)4481
34.5%
ValueCountFrequency (%)
04048
31.2%
0.29
 
0.1%
0.320
 
0.2%
0.447
 
0.4%
0.594
 
0.7%
ValueCountFrequency (%)
14.99
0.1%
12.14
 
< 0.1%
105
< 0.1%
9.612
0.1%
9.54
 
< 0.1%

openCases
Real number (ℝ≥0)

Distinct93
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.01902781
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Memory size101.5 KiB
2021-03-23T08:50:59.731576image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile20
Q153
median77
Q384
95-th percentile90
Maximum93
Range92
Interquartile range (IQR)31

Descriptive statistics

Standard deviation22.23301502
Coefficient of variation (CV)0.3317418313
Kurtosis-0.1357896058
Mean67.01902781
Median Absolute Deviation (MAD)10
Skewness-1.014172408
Sum869974
Variance494.3069569
MonotocityNot monotonic
2021-03-23T08:50:59.843264image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84622
 
4.8%
83618
 
4.8%
87591
 
4.6%
82531
 
4.1%
79506
 
3.9%
78477
 
3.7%
85466
 
3.6%
77452
 
3.5%
80403
 
3.1%
86392
 
3.0%
Other values (83)7923
61.0%
ValueCountFrequency (%)
110
0.1%
210
0.1%
311
0.1%
412
0.1%
512
0.1%
ValueCountFrequency (%)
9345
 
0.3%
92181
1.4%
91137
 
1.1%
90311
2.4%
89349
2.7%

weekday
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.964024343
Minimum0
Maximum6
Zeros1960
Zeros (%)15.1%
Memory size101.5 KiB
2021-03-23T08:50:59.935329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.012752092
Coefficient of variation (CV)0.6790605808
Kurtosis-1.251947592
Mean2.964024343
Median Absolute Deviation (MAD)2
Skewness0.03272321865
Sum38476
Variance4.051170984
MonotocityNot monotonic
2021-03-23T08:51:00.010046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
21965
15.1%
01960
15.1%
31897
14.6%
61880
14.5%
51814
14.0%
11785
13.8%
41680
12.9%
ValueCountFrequency (%)
01960
15.1%
11785
13.8%
21965
15.1%
31897
14.6%
41680
12.9%
ValueCountFrequency (%)
61880
14.5%
51814
14.0%
41680
12.9%
31897
14.6%
21965
15.1%

month
Real number (ℝ≥0)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.573068331
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size101.5 KiB
2021-03-23T08:51:00.095542image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.541237854
Coefficient of variation (CV)0.5387495878
Kurtosis-1.293258795
Mean6.573068331
Median Absolute Deviation (MAD)3
Skewness-0.01696555581
Sum85325
Variance12.54036554
MonotocityNot monotonic
2021-03-23T08:51:00.174929image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
111338
10.3%
51169
9.0%
81161
8.9%
121146
8.8%
21113
8.6%
11110
8.6%
101095
8.4%
41073
8.3%
31070
8.2%
9970
7.5%
Other values (2)1736
13.4%
ValueCountFrequency (%)
11110
8.6%
21113
8.6%
31070
8.2%
41073
8.3%
51169
9.0%
ValueCountFrequency (%)
121146
8.8%
111338
10.3%
101095
8.4%
9970
7.5%
81161
8.9%

hour
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.78899931
Minimum0
Maximum23
Zeros186
Zeros (%)1.4%
Memory size101.5 KiB
2021-03-23T08:51:00.260562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median9
Q315
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.566955313
Coefficient of variation (CV)0.5159843981
Kurtosis-0.7744644071
Mean10.78899931
Median Absolute Deviation (MAD)3
Skewness0.466381306
Sum140052
Variance30.99099146
MonotocityNot monotonic
2021-03-23T08:51:00.351081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
62095
16.1%
71606
 
12.4%
5875
 
6.7%
9691
 
5.3%
12633
 
4.9%
13621
 
4.8%
10620
 
4.8%
8536
 
4.1%
16534
 
4.1%
11532
 
4.1%
Other values (14)4238
32.6%
ValueCountFrequency (%)
0186
1.4%
1112
0.9%
2142
1.1%
3129
1.0%
4162
1.2%
ValueCountFrequency (%)
23213
1.6%
22250
1.9%
21378
2.9%
20396
3.1%
19465
3.6%

day
Real number (ℝ≥0)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.63739311
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Memory size101.5 KiB
2021-03-23T08:51:00.445201image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median15
Q324
95-th percentile30
Maximum31
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation9.001250595
Coefficient of variation (CV)0.575623477
Kurtosis-1.239768966
Mean15.63739311
Median Absolute Deviation (MAD)8
Skewness0.03932871897
Sum202989
Variance81.02251228
MonotocityNot monotonic
2021-03-23T08:51:00.537040image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2555
 
4.3%
27545
 
4.2%
25510
 
3.9%
7504
 
3.9%
11504
 
3.9%
17475
 
3.7%
12464
 
3.6%
5454
 
3.5%
1453
 
3.5%
9450
 
3.5%
Other values (21)8067
62.1%
ValueCountFrequency (%)
1453
3.5%
2555
4.3%
3375
2.9%
4381
2.9%
5454
3.5%
ValueCountFrequency (%)
31310
2.4%
30385
3.0%
29418
3.2%
28393
3.0%
27545
4.2%

timesincemidnight
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1320
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean665.5071258
Minimum0
Maximum1439
Zeros5
Zeros (%)< 0.1%
Memory size101.5 KiB
2021-03-23T08:51:00.642298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile276
Q1360
median593
Q3932
95-th percentile1290
Maximum1439
Range1439
Interquartile range (IQR)572

Descriptive statistics

Standard deviation341.885553
Coefficient of variation (CV)0.5137218517
Kurtosis-0.8376501861
Mean665.5071258
Median Absolute Deviation (MAD)233
Skewness0.4684004963
Sum8638948
Variance116885.7313
MonotocityNot monotonic
2021-03-23T08:51:00.751961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3601934
 
14.9%
4201350
 
10.4%
300769
 
5.9%
540215
 
1.7%
600156
 
1.2%
780152
 
1.2%
720144
 
1.1%
48096
 
0.7%
66053
 
0.4%
84053
 
0.4%
Other values (1310)8059
62.1%
ValueCountFrequency (%)
05
< 0.1%
14
< 0.1%
33
< 0.1%
56
< 0.1%
66
< 0.1%
ValueCountFrequency (%)
14393
< 0.1%
14385
< 0.1%
14373
< 0.1%
14363
< 0.1%
14352
 
< 0.1%

timesincelast
Real number (ℝ≥0)

ZEROS

Distinct3973
Distinct (%)30.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39465.61675
Minimum0
Maximum1651500
Zeros4748
Zeros (%)36.6%
Memory size101.5 KiB
2021-03-23T08:51:01.001959image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median209
Q324091
95-th percentile211023
Maximum1651500
Range1651500
Interquartile range (IQR)24091

Descriptive statistics

Standard deviation93906.09122
Coefficient of variation (CV)2.379440611
Kurtosis39.54821985
Mean39465.61675
Median Absolute Deviation (MAD)209
Skewness4.737115827
Sum512303171
Variance8818353969
MonotocityNot monotonic
2021-03-23T08:51:01.109031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04748
36.6%
86400514
 
4.0%
172800309
 
2.4%
259200112
 
0.9%
34560062
 
0.5%
1557
 
0.4%
156
 
0.4%
1440051
 
0.4%
450
 
0.4%
1647
 
0.4%
Other values (3963)6975
53.7%
ValueCountFrequency (%)
04748
36.6%
156
 
0.4%
27
 
0.1%
326
 
0.2%
450
 
0.4%
ValueCountFrequency (%)
16515001
< 0.1%
16506001
< 0.1%
13596911
< 0.1%
13068002
< 0.1%
12222001
< 0.1%

timesincestart
Real number (ℝ≥0)

ZEROS

Distinct7115
Distinct (%)54.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean284102.2409
Minimum0
Maximum9880969
Zeros794
Zeros (%)6.1%
Memory size101.5 KiB
2021-03-23T08:51:01.227203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11326
median17467
Q3303259
95-th percentile1307207
Maximum9880969
Range9880969
Interquartile range (IQR)301933

Descriptive statistics

Standard deviation664595.6238
Coefficient of variation (CV)2.339283287
Kurtosis48.1515808
Mean284102.2409
Median Absolute Deviation (MAD)17467
Skewness5.712790432
Sum3687931189
Variance4.416873432 × 1011
MonotocityNot monotonic
2021-03-23T08:51:01.342723image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0794
 
6.1%
113712
 
0.1%
61910
 
0.1%
156910
 
0.1%
75910
 
0.1%
172110
 
0.1%
13089
 
0.1%
3259
 
0.1%
15529
 
0.1%
14579
 
0.1%
Other values (7105)12099
93.2%
ValueCountFrequency (%)
0794
6.1%
31
 
< 0.1%
141
 
< 0.1%
161
 
< 0.1%
271
 
< 0.1%
ValueCountFrequency (%)
98809691
< 0.1%
92689692
< 0.1%
89233691
< 0.1%
86641692
< 0.1%
83185692
< 0.1%

remainingtime
Real number (ℝ≥0)

ZEROS

Distinct7844
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3234691.769
Minimum0
Maximum36488789
Zeros484
Zeros (%)3.7%
Memory size101.5 KiB
2021-03-23T08:51:01.458312image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile28391
Q1356400
median857143
Q33342000
95-th percentile15571534
Maximum36488789
Range36488789
Interquartile range (IQR)2985600

Descriptive statistics

Standard deviation5572039.194
Coefficient of variation (CV)1.722587372
Kurtosis9.89562206
Mean3234691.769
Median Absolute Deviation (MAD)651353
Skewness2.936383918
Sum4.198953386 × 1010
Variance3.104762077 × 1013
MonotocityNot monotonic
2021-03-23T08:51:01.570905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0484
 
3.7%
9360036
 
0.3%
18000030
 
0.2%
28080029
 
0.2%
27000024
 
0.2%
35640024
 
0.2%
52920023
 
0.2%
26640023
 
0.2%
10800023
 
0.2%
9720022
 
0.2%
Other values (7834)12263
94.5%
ValueCountFrequency (%)
0484
3.7%
8101
 
< 0.1%
11971
 
< 0.1%
12402
 
< 0.1%
14201
 
< 0.1%
ValueCountFrequency (%)
364887891
 
< 0.1%
364885731
 
< 0.1%
364885541
 
< 0.1%
364879383
< 0.1%
364820271
 
< 0.1%

OrderOfEvent
Real number (ℝ≥0)

Distinct185
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.23388029
Minimum1
Maximum185
Zeros0
Zeros (%)0.0%
Memory size101.5 KiB
2021-03-23T08:51:01.687289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median9
Q314
95-th percentile37
Maximum185
Range184
Interquartile range (IQR)9

Descriptive statistics

Standard deviation18.77234865
Coefficient of variation (CV)1.41850676
Kurtosis29.76908221
Mean13.23388029
Median Absolute Deviation (MAD)5
Skewness4.891276052
Sum171789
Variance352.4010739
MonotocityNot monotonic
2021-03-23T08:51:01.794074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1776
 
6.0%
2776
 
6.0%
3776
 
6.0%
4776
 
6.0%
5776
 
6.0%
6774
 
6.0%
7771
 
5.9%
8757
 
5.8%
9741
 
5.7%
10726
 
5.6%
Other values (175)5332
41.1%
ValueCountFrequency (%)
1776
6.0%
2776
6.0%
3776
6.0%
4776
6.0%
5776
6.0%
ValueCountFrequency (%)
1851
< 0.1%
1841
< 0.1%
1831
< 0.1%
1821
< 0.1%
1811
< 0.1%

label
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size101.5 KiB
regular
12945 
deviant
 
36

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters90867
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular
ValueCountFrequency (%)
regular12945
99.7%
deviant36
 
0.3%
2021-03-23T08:51:01.980840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category
2021-03-23T08:51:02.035600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
ValueCountFrequency (%)
regular12945
99.7%
deviant36
 
0.3%

Most occurring characters

ValueCountFrequency (%)
r25890
28.5%
e12981
14.3%
a12981
14.3%
g12945
14.2%
u12945
14.2%
l12945
14.2%
d36
 
< 0.1%
v36
 
< 0.1%
i36
 
< 0.1%
n36
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter90867
100.0%

Most frequent character per category

ValueCountFrequency (%)
r25890
28.5%
e12981
14.3%
a12981
14.3%
g12945
14.2%
u12945
14.2%
l12945
14.2%
d36
 
< 0.1%
v36
 
< 0.1%
i36
 
< 0.1%
n36
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin90867
100.0%

Most frequent character per script

ValueCountFrequency (%)
r25890
28.5%
e12981
14.3%
a12981
14.3%
g12945
14.2%
u12945
14.2%
l12945
14.2%
d36
 
< 0.1%
v36
 
< 0.1%
i36
 
< 0.1%
n36
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII90867
100.0%

Most frequent character per block

ValueCountFrequency (%)
r25890
28.5%
e12981
14.3%
a12981
14.3%
g12945
14.2%
u12945
14.2%
l12945
14.2%
d36
 
< 0.1%
v36
 
< 0.1%
i36
 
< 0.1%
n36
 
< 0.1%

Interactions

2021-03-23T08:50:32.330646image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:32.437941image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:32.535794image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:32.635798image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:32.729819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:32.827995image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:32.923886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:33.017509image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:33.255967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:33.357841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:33.458161image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:33.555583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:33.658301image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:33.759433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:33.862422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:33.963178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.066560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.163625image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.264340image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.363217image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.459647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.560422image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.665565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.769899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.871407image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:34.977689image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.082305image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.182961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.284013image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.380571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.471520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.565366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.657459image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.746883image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.841399image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:35.939886image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:36.038167image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:36.269329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:36.368513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:36.465350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:36.564176image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:36.665915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:36.764363image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:36.857129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:36.953424image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.047348image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.139464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.236130image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.336310image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.437278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.538999image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.644831image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.744595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.834949image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:37.928359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.016125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.106446image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.193915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.280985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.364038image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.451812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.543411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.633978image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.721842image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.814541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:38.905289image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.001851image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.239721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.334641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.430768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.520819image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.612694image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.701961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.796484image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.894735image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:39.991865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.086540image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.185638image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.282604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.375935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.472875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.563853image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.656716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.743840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.834976image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:40.921096image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.011767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.106781image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.201046image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.292134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.388716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.482705image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.572815image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.666255image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.753686image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.843717image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:41.927467image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.014762image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.229884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.316917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.407997image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.498224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.585739image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.677607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.767595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.864378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:42.963150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.056729image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.152361image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.247152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.340125image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.431825image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.520607image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.617779image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.713809image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.807876image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:43.905847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.002411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.102560image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.208975image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.307554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.408325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.502133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.599548image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.694566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.787784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.885350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:44.985691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:45.226092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:45.328915image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:45.429584image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:45.528718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:45.631643image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:45.728675image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:45.827749image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:45.922338image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.019133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.113701image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.206005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.302936image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.404396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.504644image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.606227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.705918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.802080image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.900939image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:46.994447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.090397image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.179927image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.272795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.363671image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.454622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.547912image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.644921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.741148image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.839727image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:47.936168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:48.039294image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:48.274208image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:48.374441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:48.476336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:48.572292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:48.671647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:48.769259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:48.864073image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:48.964732image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.069382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.172879image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.273474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.376019image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.475691image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.578820image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.677015image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.776804image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.870132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:49.967215image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:50.062352image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:50.154659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:50.251617image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:50.352513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:50.452537image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-03-23T08:50:50.551005image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-03-23T08:51:02.101249image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-03-23T08:51:02.271533image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-03-23T08:51:02.440144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-03-23T08:51:02.650935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-03-23T08:51:02.980566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-03-23T08:50:50.847670image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-03-23T08:50:52.178204image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

InfectionSuspectedorg:groupDiagnosticBloodDisfuncOrgSIRSCritTachypneaHypotensieSIRSCritHeartRateInfusionDiagnosticArtAstrupconcept:nameAgeDiagnosticICDiagnosticSputumDiagnosticLiquorDiagnosticOtherSIRSCriteria2OrMoreDiagnosticXthoraxSIRSCritTemperaturetime:timestampDiagnosticUrinaryCultureSIRSCritLeucosOligurieDiagnosticLacticAcidlifecycle:transitionDiagnoseHypoxieDiagnosticUrinarySedimentDiagnosticECGcaseLeucocytesCRPLacticAcidopenCasesweekdaymonthhourdaytimesincemidnighttimesincelasttimesincestartremainingtimeOrderOfEventlabel
0TrueATrueTrueTrueTrueTrueTrueTrueER Registration85.0TrueFalseFalseFalseTrueTrueTrue2014-10-22 09:15:41+00:00TrueFalseFalseTruecompleteotherFalseTrueTrueA0.00.00.081.0210.09.022.0555.00.00.0968359.01.0regular
1otherBotherotherotherotherotherotherotherLeucocytes85.0otherotherotherotherotherotherother2014-10-22 09:27:00+00:00otherotherotherothercompleteotherotherotherotherA9.60.00.081.0210.09.022.0567.00.0679.0967680.02.0regular
2otherBotherotherotherotherotherotherotherCRP85.0otherotherotherotherotherotherother2014-10-22 09:27:00+00:00otherotherotherothercompleteotherotherotherotherA9.621.00.081.0210.09.022.0567.00.0679.0967680.03.0regular
3otherBotherotherotherotherotherotherotherLacticAcid85.0otherotherotherotherotherotherother2014-10-22 09:27:00+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.09.022.0567.0679.0679.0967680.04.0regular
4otherCotherotherotherotherotherotherotherER Triage85.0otherotherotherotherotherotherother2014-10-22 09:33:37+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.09.022.0573.0397.01076.0967283.05.0regular
5otherAotherotherotherotherotherotherotherER Sepsis Triage85.0otherotherotherotherotherotherother2014-10-22 09:34:00+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.09.022.0574.023.01099.0967260.06.0regular
6otherAotherotherotherotherotherotherotherIV Liquid85.0otherotherotherotherotherotherother2014-10-22 12:03:47+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.012.022.0723.00.010086.0958273.07.0regular
7otherAotherotherotherotherotherotherotherIV Antibiotics85.0otherotherotherotherotherotherother2014-10-22 12:03:47+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.012.022.0723.08987.010086.0958273.08.0regular
8otherDotherotherotherotherotherotherotherAdmission NC85.0otherotherotherotherotherotherother2014-10-22 12:13:19+00:00otherotherotherothercompleteotherotherotherotherA9.621.02.281.0210.012.022.0733.0572.010658.0957701.09.0regular
9otherBotherotherotherotherotherotherotherCRP85.0otherotherotherotherotherotherother2014-10-24 07:00:00+00:00otherotherotherothercompleteotherotherotherotherA9.6109.02.279.0410.07.024.0420.00.0164659.0803700.010.0regular

Last rows

InfectionSuspectedorg:groupDiagnosticBloodDisfuncOrgSIRSCritTachypneaHypotensieSIRSCritHeartRateInfusionDiagnosticArtAstrupconcept:nameAgeDiagnosticICDiagnosticSputumDiagnosticLiquorDiagnosticOtherSIRSCriteria2OrMoreDiagnosticXthoraxSIRSCritTemperaturetime:timestampDiagnosticUrinaryCultureSIRSCritLeucosOligurieDiagnosticLacticAcidlifecycle:transitionDiagnoseHypoxieDiagnosticUrinarySedimentDiagnosticECGcaseLeucocytesCRPLacticAcidopenCasesweekdaymonthhourdaytimesincemidnighttimesincelasttimesincestartremainingtimeOrderOfEventlabel
12971otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-15 15:08:00+00:00otherotherotherothercompleteotherotherotherothermissing19.0207.01.075.0511.015.015.0908.0205680.0483761.0598920.015.0regular
12972otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-15 18:42:00+00:00otherotherotherothercompleteotherotherotherothermissing17.1207.01.076.0511.018.015.01122.012840.0496601.0586080.016.0regular
12973otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-15 22:46:00+00:00otherotherotherothercompleteotherotherotherothermissing17.7207.01.076.0511.022.015.01366.014640.0511241.0571440.017.0regular
12974otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-16 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.2207.01.077.0611.06.016.0360.00.0537281.0545400.018.0regular
12975otherBotherotherotherotherotherotherotherCRP90.0otherotherotherotherotherotherother2014-11-16 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.278.01.077.0611.06.016.0360.026040.0537281.0545400.019.0regular
12976otherBotherotherotherotherotherotherotherLacticAcid90.0otherotherotherotherotherotherother2014-11-16 12:05:00+00:00otherotherotherothercompleteotherotherotherothermissing17.278.01.775.0611.012.016.0725.021900.0559181.0523500.020.0regular
12977otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-17 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.478.01.777.0011.06.017.0360.00.0623681.0459000.021.0regular
12978otherBotherotherotherotherotherotherotherCRP90.0otherotherotherotherotherotherother2014-11-17 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing17.464.01.777.0011.06.017.0360.064500.0623681.0459000.022.0regular
12979otherBotherotherotherotherotherotherotherLeucocytes90.0otherotherotherotherotherotherother2014-11-18 06:00:00+00:00otherotherotherothercompleteotherotherotherothermissing16.864.01.778.0111.06.018.0360.086400.0710081.0372600.023.0regular
12980otherEotherotherotherotherotherotherotherRelease C90.0otherotherotherotherotherotherother2014-11-22 13:30:00+00:00otherotherotherothercompleteotherotherotherothermissing16.864.01.770.0511.013.022.0810.0372600.01082681.00.024.0regular